def _single_test(net, pars, sample):
vol_ins, lbl_outs, msks = sample.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward(vol_ins)
# cost function and accumulate errors
props, err, grdts = pars['cost_fn'](props, lbl_outs)
cls = cost_fn.get_cls(props, lbl_outs)
return props, err, cls
def _single_test(net, pars, sample):
vol_ins, lbl_outs, msks = sample.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward( vol_ins )
# cost function and accumulate errors
props, err, grdts = pars['cost_fn']( props, lbl_outs )
cls = cost_fn.get_cls(props, lbl_outs)
return props, err, cls
def _single_test(net, pars, sample):
vol_ins, lbl_outs, msks, wmsks = sample.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward( vol_ins )
# cost function and accumulate errors
props, err, grdts = pars['cost_fn']( props, lbl_outs )
cls = cost_fn.get_cls(props, lbl_outs)
re = 0.0
malis_cls = 0.0
if pars['is_malis']:
malis_weights, rand_errors = cost_fn.malis_weight( pars, props, lbl_outs )
re = rand_errors.values()[0]
# dictionary of malis classification error
mcd = utils.get_malis_cls( props, lbl_outs, malis_weights )
malis_cls = mcd.values()[0]
return props, err, cls, re, malis_cls
def _single_test(net, pars, sample):
vol_ins, lbl_outs, msks, wmsks = sample.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins)
props = net.forward( vol_ins )
# cost function and accumulate errors
props, err, grdts = pars['cost_fn']( props, lbl_outs )
# pixel classification error
cls = cost_fn.get_cls(props, lbl_outs)
# rand error
re = pyznn.get_rand_error(props.values()[0], lbl_outs.values()[0])
malis_cls = 0.0
malis_eng = 0.0
if pars['is_malis']:
malis_weights, rand_errors, num_non_bdr = cost_fn.malis_weight( pars, props, lbl_outs )
# dictionary of malis classification error
dmc, dme = utils.get_malis_cost( props, lbl_outs, malis_weights )
malis_cls = dmc.values()[0]
malis_eng = dme.values()[0]
return props, err, cls, re, malis_cls, malis_eng
def main( conf_file='config.cfg', logfile=None ):
#%% parameters
print "reading config parameters..."
config, pars = front_end.parser( conf_file )
if pars.has_key('logging') and pars['logging']:
print "recording configuration file..."
front_end.record_config_file( pars )
logfile = front_end.make_logfile_name( pars )
#%% create and initialize the network
if pars['train_load_net'] and os.path.exists(pars['train_load_net']):
print "loading network..."
net = netio.load_network( pars )
# load existing learning curve
lc = zstatistics.CLearnCurve( pars['train_load_net'] )
else:
if pars['train_seed_net'] and os.path.exists(pars['train_seed_net']):
print "seeding network..."
net = netio.load_network( pars, is_seed=True )
else:
print "initializing network..."
net = netio.init_network( pars )
# initalize a learning curve
lc = zstatistics.CLearnCurve()
# show field of view
print "field of view: ", net.get_fov()
print "output volume info: ", net.get_outputs_setsz()
# set some parameters
print 'setting up the network...'
vn = utils.get_total_num(net.get_outputs_setsz())
eta = pars['eta'] #/ vn
net.set_eta( eta )
net.set_momentum( pars['momentum'] )
net.set_weight_decay( pars['weight_decay'] )
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = front_end.CSamples(config, pars, pars['train_range'], net, outsz, logfile)
print "\n\ncreate test samples..."
smp_tst = front_end.CSamples(config, pars, pars['test_range'], net, outsz, logfile)
# initialization
elapsed = 0
err = 0
cls = 0
# interactive visualization
plt.ion()
plt.show()
# the last iteration we want to continue training
iter_last = lc.get_last_it()
print "start training..."
start = time.time()
print "start from ", iter_last+1
for i in xrange(iter_last+1, pars['Max_iter']+1):
vol_ins, lbl_outs, msks = smp_trn.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward( vol_ins )
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn']( props, lbl_outs )
err = err + cerr
cls = cls + cost_fn.get_cls(props, lbl_outs)
# mask process the gradient
grdts = utils.dict_mul(grdts, msks)
# run backward pass
grdts = utils.make_continuous(grdts, dtype=pars['dtype'])
net.backward( grdts )
if pars['is_malis'] :
malis_weights = cost_fn.malis_weight(props, lbl_outs)
grdts = utils.dict_mul(grdts, malis_weights)
if i%pars['Num_iter_per_test']==0:
# test the net
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
if i%pars['Num_iter_per_show']==0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
# normalize
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
lc.append_train(i, err, cls)
# time
elapsed = time.time() - start
elapsed = elapsed / pars['Num_iter_per_show']
show_string = "iteration %d, err: %.3f, cls: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
if pars['is_visual']:
# show results To-do: run in a separate thread
front_end.inter_show(start, lc, eta, vol_ins, props, lbl_outs, grdts, pars)
if pars['is_rebalance'] and 'aff' not in pars['out_type']:
plt.subplot(247)
plt.imshow(msks.values()[0][0,0,:,:], interpolation='nearest', cmap='gray')
plt.xlabel('rebalance weight')
if pars['is_malis']:
plt.subplot(248)
plt.imshow(malis_weights.values()[0][0,0,:,:], interpolation='nearest', cmap='gray')
plt.xlabel('malis weight (log)')
plt.pause(2)
plt.show()
# reset err and cls
err = 0
cls = 0
# reset time
start = time.time()
if i%pars['Num_iter_per_save']==0:
# save network
netio.save_network(net, pars['train_save_net'], num_iters=i)
lc.save( pars, elapsed )
def main(args):
config, pars, logfile = parse_args(args)
#%% create and initialize the network
net, lc = znetio.create_net(pars)
# total voxel number of output volumes
vn = utils.get_total_num(net.get_outputs_setsz())
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = zsample.CSamples(config, pars, pars['train_range'], net, outsz,
logfile)
print "\n\ncreate test samples..."
smp_tst = zsample.CSamples(config, pars, pars['test_range'], net, outsz,
logfile)
if pars['is_check']:
import zcheck
zcheck.check_patch(pars, smp_trn)
# gradient check is not working now.
# zcheck.check_gradient(pars, net, smp_trn)
# initialization
eta = pars['eta']
elapsed = 0
err = 0.0 # cost energy
cls = 0.0 # pixel classification error
re = 0.0 # rand error
# number of voxels which accumulate error
# (if a mask exists)
num_mask_voxels = 0
if pars['is_malis']:
malis_cls = 0.0
malis_eng = 0.0
else:
malis_weights = None
# the last iteration we want to continue training
iter_last = lc.get_last_it()
print "start training..."
start = time.time()
total_time = 0.0
print "start from ", iter_last + 1
#Saving initial/seeded network
# get file name
fname, fname_current = znetio.get_net_fname(pars['train_net_prefix'],
iter_last,
suffix="init")
znetio.save_network(net, fname, pars['is_stdio'])
lc.save(pars, fname, elapsed=0.0, suffix="init_iter{}".format(iter_last))
# no nan detected
nonan = True
for i in xrange(iter_last + 1, pars['Max_iter'] + 1):
# time cumulation
total_time += time.time() - start
start = time.time()
# get random sub volume from sample
vol_ins, lbl_outs, msks, wmsks = smp_trn.get_random_sample()
# forward pass
# apply the transformations in memory rather than array view
vol_ins = utils.make_continuous(vol_ins)
props = net.forward(vol_ins)
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn'](props, lbl_outs, msks)
err += cerr
cls += cost_fn.get_cls(props, lbl_outs)
# compute rand error
if pars['is_debug']:
assert not np.all(lbl_outs.values()[0] == 0)
re += pyznn.get_rand_error(props.values()[0], lbl_outs.values()[0])
num_mask_voxels += utils.sum_over_dict(msks)
# check whether there is a NaN here!
if pars['is_debug']:
nonan = nonan and utils.check_dict_nan(vol_ins)
nonan = nonan and utils.check_dict_nan(lbl_outs)
nonan = nonan and utils.check_dict_nan(msks)
nonan = nonan and utils.check_dict_nan(wmsks)
nonan = nonan and utils.check_dict_nan(props)
nonan = nonan and utils.check_dict_nan(grdts)
if not nonan:
utils.inter_save(pars, net, lc, vol_ins, props, lbl_outs, \
grdts, malis_weights, wmsks, elapsed, i)
# stop training
return
# gradient reweighting
grdts = utils.dict_mul(grdts, msks)
if pars['rebalance_mode']:
grdts = utils.dict_mul(grdts, wmsks)
if pars['is_malis']:
malis_weights, rand_errors, num_non_bdr = cost_fn.malis_weight(
pars, props, lbl_outs)
if num_non_bdr <= 1:
# skip this iteration
continue
grdts = utils.dict_mul(grdts, malis_weights)
dmc, dme = utils.get_malis_cost(props, lbl_outs, malis_weights)
malis_cls += dmc.values()[0]
malis_eng += dme.values()[0]
# run backward pass
grdts = utils.make_continuous(grdts)
net.backward(grdts)
total_time += time.time() - start
start = time.time()
if i % pars['Num_iter_per_show'] == 0:
# time
elapsed = total_time / pars['Num_iter_per_show']
# normalize
if utils.dict_mask_empty(msks):
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
else:
err = err / num_mask_voxels / pars['Num_iter_per_show']
cls = cls / num_mask_voxels / pars['Num_iter_per_show']
re = re / pars['Num_iter_per_show']
lc.append_train(i, err, cls, re)
if pars['is_malis']:
malis_cls = malis_cls / pars['Num_iter_per_show']
malis_eng = malis_eng / pars['Num_iter_per_show']
lc.append_train_malis_cls(malis_cls)
lc.append_train_malis_eng(malis_eng)
show_string = "update %d, cost: %.3f, pixel error: %.3f, rand error: %.3f, me: %.3f, mc: %.3f, elapsed: %.1f s/iter, learning rate: %.5f"\
%(i, err, cls, re, malis_eng, malis_cls, elapsed, eta )
else:
show_string = "update %d, cost: %.3f, pixel error: %.3f, rand error: %.3f, elapsed: %.1f s/iter, learning rate: %.5f"\
%(i, err, cls, re, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
# reset err and cls
err = 0
cls = 0
re = 0
num_mask_voxels = 0
if pars['is_malis']:
malis_cls = 0
# reset time
total_time = 0
start = time.time()
# test the net
if i % pars['Num_iter_per_test'] == 0:
# time accumulation should skip the test
total_time += time.time() - start
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
start = time.time()
if i % pars['Num_iter_per_save'] == 0:
utils.inter_save(pars, net, lc, vol_ins, props, lbl_outs, \
grdts, malis_weights, wmsks, elapsed, i)
if i % pars['Num_iter_per_annealing'] == 0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
# stop the iteration at checking mode
if pars['is_check']:
print "only need one iteration for checking, stop program..."
break
def main( conf_file='config.cfg', logfile=None ):
#%% parameters
print "reading config parameters..."
config, pars = zconfig.parser( conf_file )
if pars.has_key('logging') and pars['logging']:
print "recording configuration file..."
zlog.record_config_file( pars )
logfile = zlog.make_logfile_name( pars )
#%% create and initialize the network
if pars['train_load_net'] and os.path.exists(pars['train_load_net']):
print "loading network..."
net = znetio.load_network( pars )
# load existing learning curve
lc = zstatistics.CLearnCurve( pars['train_load_net'] )
# the last iteration we want to continue training
iter_last = lc.get_last_it()
else:
if pars['train_seed_net'] and os.path.exists(pars['train_seed_net']):
print "seeding network..."
net = znetio.load_network( pars, is_seed=True )
else:
print "initializing network..."
net = znetio.init_network( pars )
# initalize a learning curve
lc = zstatistics.CLearnCurve()
iter_last = lc.get_last_it()
# show field of view
print "field of view: ", net.get_fov()
# total voxel number of output volumes
vn = utils.get_total_num(net.get_outputs_setsz())
# set some parameters
print 'setting up the network...'
eta = pars['eta']
net.set_eta( pars['eta'] )
net.set_momentum( pars['momentum'] )
net.set_weight_decay( pars['weight_decay'] )
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = zsample.CSamples(config, pars, pars['train_range'], net, outsz, logfile)
print "\n\ncreate test samples..."
smp_tst = zsample.CSamples(config, pars, pars['test_range'], net, outsz, logfile)
# initialization
elapsed = 0
err = 0.0 # cost energy
cls = 0.0 # pixel classification error
re = 0.0 # rand error
# number of voxels which accumulate error
# (if a mask exists)
num_mask_voxels = 0
if pars['is_malis']:
malis_cls = 0.0
print "start training..."
start = time.time()
total_time = 0.0
print "start from ", iter_last+1
#Saving initialized network
if iter_last+1 == 1:
znetio.save_network(net, pars['train_save_net'], num_iters=0)
lc.save( pars, 0.0 )
for i in xrange(iter_last+1, pars['Max_iter']+1):
# get random sub volume from sample
vol_ins, lbl_outs, msks, wmsks = smp_trn.get_random_sample()
# forward pass
# apply the transformations in memory rather than array view
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward( vol_ins )
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn']( props, lbl_outs, msks )
err += cerr
cls += cost_fn.get_cls(props, lbl_outs)
num_mask_voxels += utils.sum_over_dict(msks)
# gradient reweighting
grdts = utils.dict_mul( grdts, msks )
grdts = utils.dict_mul( grdts, wmsks )
if pars['is_malis'] :
malis_weights, rand_errors = cost_fn.malis_weight(pars, props, lbl_outs)
grdts = utils.dict_mul(grdts, malis_weights)
# accumulate the rand error
re += rand_errors.values()[0]
malis_cls_dict = utils.get_malis_cls( props, lbl_outs, malis_weights )
malis_cls += malis_cls_dict.values()[0]
total_time += time.time() - start
start = time.time()
# test the net
if i%pars['Num_iter_per_test']==0:
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
if i%pars['Num_iter_per_show']==0:
# normalize
if utils.dict_mask_empty(msks):
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
else:
err = err / num_mask_voxels / pars['Num_iter_per_show']
cls = cls / num_mask_voxels / pars['Num_iter_per_show']
lc.append_train(i, err, cls)
# time
elapsed = total_time / pars['Num_iter_per_show']
if pars['is_malis']:
re = re / pars['Num_iter_per_show']
lc.append_train_rand_error( re )
malis_cls = malis_cls / pars['Num_iter_per_show']
lc.append_train_malis_cls( malis_cls )
show_string = "iteration %d, err: %.3f, cls: %.3f, re: %.6f, mc: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, re, malis_cls, elapsed, eta )
else:
show_string = "iteration %d, err: %.3f, cls: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
# reset err and cls
err = 0
cls = 0
re = 0
num_mask_voxels = 0
if pars['is_malis']:
malis_cls = 0
# reset time
total_time = 0
start = time.time()
if i%pars['Num_iter_per_annealing']==0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
if i%pars['Num_iter_per_save']==0:
# save network
znetio.save_network(net, pars['train_save_net'], num_iters=i)
lc.save( pars, elapsed )
if pars['is_malis']:
utils.save_malis(malis_weights, pars['train_save_net'], num_iters=i)
# run backward pass
grdts = utils.make_continuous(grdts, dtype=pars['dtype'])
net.backward( grdts )
def main(conf_file='config.cfg', logfile=None):
#%% parameters
print "reading config parameters..."
config, pars = front_end.parser(conf_file)
if pars.has_key('logging') and pars['logging']:
print "recording configuration file..."
front_end.record_config_file(pars)
logfile = front_end.make_logfile_name(pars)
#%% create and initialize the network
if pars['train_load_net'] and os.path.exists(pars['train_load_net']):
print "loading network..."
net = netio.load_network(pars)
# load existing learning curve
lc = zstatistics.CLearnCurve(pars['train_load_net'])
else:
if pars['train_seed_net'] and os.path.exists(pars['train_seed_net']):
print "seeding network..."
net = netio.seed_network(pars, is_seed=True)
else:
print "initializing network..."
net = netio.init_network(pars)
# initalize a learning curve
lc = zstatistics.CLearnCurve()
# show field of view
print "field of view: ", net.get_fov()
print "output volume info: ", net.get_outputs_setsz()
# set some parameters
print 'setting up the network...'
vn = utils.get_total_num(net.get_outputs_setsz())
eta = pars['eta'] #/ vn
net.set_eta(eta)
net.set_momentum(pars['momentum'])
net.set_weight_decay(pars['weight_decay'])
# initialize samples
outsz = pars['train_outsz']
print "\n\ncreate train samples..."
smp_trn = front_end.CSamples(config, pars, pars['train_range'], net, outsz,
logfile)
print "\n\ncreate test samples..."
smp_tst = front_end.CSamples(config, pars, pars['test_range'], net, outsz,
logfile)
# initialization
elapsed = 0
err = 0
cls = 0
# interactive visualization
plt.ion()
plt.show()
# the last iteration we want to continue training
iter_last = lc.get_last_it()
print "start training..."
start = time.time()
print "start from ", iter_last + 1
for i in xrange(iter_last + 1, pars['Max_iter'] + 1):
vol_ins, lbl_outs, msks = smp_trn.get_random_sample()
# forward pass
vol_ins = utils.make_continuous(vol_ins, dtype=pars['dtype'])
props = net.forward(vol_ins)
# cost function and accumulate errors
props, cerr, grdts = pars['cost_fn'](props, lbl_outs)
err = err + cerr
cls = cls + cost_fn.get_cls(props, lbl_outs)
# mask process the gradient
grdts = utils.dict_mul(grdts, msks)
# run backward pass
grdts = utils.make_continuous(grdts, dtype=pars['dtype'])
net.backward(grdts)
if pars['is_malis']:
malis_weights = cost_fn.malis_weight(props, lbl_outs)
grdts = utils.dict_mul(grdts, malis_weights)
if i % pars['Num_iter_per_test'] == 0:
# test the net
lc = test.znn_test(net, pars, smp_tst, vn, i, lc)
if i % pars['Num_iter_per_show'] == 0:
# anneal factor
eta = eta * pars['anneal_factor']
net.set_eta(eta)
# normalize
err = err / vn / pars['Num_iter_per_show']
cls = cls / vn / pars['Num_iter_per_show']
lc.append_train(i, err, cls)
# time
elapsed = time.time() - start
elapsed = elapsed / pars['Num_iter_per_show']
show_string = "iteration %d, err: %.3f, cls: %.3f, elapsed: %.1f s/iter, learning rate: %.6f"\
%(i, err, cls, elapsed, eta )
if pars.has_key('logging') and pars['logging']:
utils.write_to_log(logfile, show_string)
print show_string
if pars['is_visual']:
# show results To-do: run in a separate thread
front_end.inter_show(start, lc, eta, vol_ins, props, lbl_outs,
grdts, pars)
if pars['is_rebalance'] and 'aff' not in pars['out_type']:
plt.subplot(247)
plt.imshow(msks.values()[0][0, 0, :, :],
interpolation='nearest',
cmap='gray')
plt.xlabel('rebalance weight')
if pars['is_malis']:
plt.subplot(248)
plt.imshow(malis_weights.values()[0][0, 0, :, :],
interpolation='nearest',
cmap='gray')
plt.xlabel('malis weight (log)')
plt.pause(2)
plt.show()
# reset err and cls
err = 0
cls = 0
# reset time
start = time.time()
if i % pars['Num_iter_per_save'] == 0:
# save network
netio.save_network(net, pars['train_save_net'], num_iters=i)
lc.save(pars, elapsed)